Package geneSLOPE performes genome-wide association study (GWAS) with SLOPE, short for Sorted L-One Penalized Estimation. SLOPE is a method for estimating the vector of coefficients in linear model. For details about it see references.
GWAS is splitted into three steps.
In the first step data is read using bigmemory package and immediatly screened using marginal tests for each SNP
SNPs are clumped based on their correlations
SLOPE is performed on data where each clump has one representative (therefore we ensure that variables in linear model are not strognly correlated)
Malgorzata Bogdan, Damian Brzyski, Emmanuel J. Candes, Christine Peterson, Chiara Sabatti, Piotr Sobczyk
Maintainer: Piotr Sobczyk firstname.lastname@example.org
SLOPE – Adaptive Variable Selection via Convex Optimization, Malgorzata Bogdan, Ewout van den Berg, Chiara Sabatti, Weijie Su and Emmanuel Candes
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famFile <- system.file("extdata", "plinkPhenotypeExample.fam", package = "geneSLOPE") mapFile <- system.file("extdata", "plinkMapExample.map", package = "geneSLOPE") snpsFile <- system.file("extdata", "plinkDataExample.raw", package = "geneSLOPE") phe <- read_phenotype(filename = famFile) screening.result <- screen_snps(snpsFile, mapFile, phe, pValMax = 0.05, chunkSize = 1e2) clumping.result <- clump_snps(screening.result, rho = 0.3, verbose = TRUE) slope.result <- select_snps(clumping.result, fdr=0.1) ## Not run: gui_geneSLOPE() ## End(Not run)
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